Michael Bright presented on using MongoDB and Python. Some key points: 1) MongoDB is a document-oriented NoSQL database that uses JSON-like documents with dynamic schemas, horizontal scaling, and high performance. It provides an alternative to relational databases for applications that need flexibility and scalability. 2) PyMongo is the main Python driver for working with MongoDB, but there are also frameworks and ORMs that provide higher-level APIs. Basic operations like inserting, finding, updating, and deleting documents can be done from the Python shell or code. 3) MongoDB supports indexing, sorting, projections and aggregation to optimize queries. The aggregation framework provides data processing pipelines to transform and analyze data in MongoDB.

1. Using MongoDB and Python
Michael Bright
Python User Group, Grenoble
23 Fevrier 2016

2. Databases

3. Databases
Relational Databases (RDBMS) “Les SGBDRs” (OLTP – Online Transaction Processing)
• Based on relational database model invented by E.F. Codd (IBM) in 1970
• SQL (SEQUEL): expressive query language
• Achieves performance increase through “vertical scaling” (higher performance node)
NoSQL (No SQL, or “Not just SQL”?) existed before RDBMS, resurged out of a need for more Scalability for
Web2.0
- Columnar, e.g. Cassandra, Hbase, Vertica
- Document Oriented, e.g. CouchDB, MongoDB, RethinkDB
- Graph, e.g. Neo4J
- Key-value, e.g. CouchDB, Dynamo, Riak, Redis, Oracle NoSQL, MUMPS
- Multi-model, e.g. Alchemy, ArangoDB
• Achieves performance increase through “horizontal scaling” (by adding nodes)
OLAP – Online Analytic Processing

4. Databases for scalability
The need for scalability requires distribution of processing making it more difficult
to satisfy all criteria, according to the CAP Theorem (2000)
- Consistency (Atomicity)
- Availability (A request will always receive a response)
- Partition Tolerance (if nodes become disconnected, the system continues to
function)
It is impossible to satisfy all three constraints (proved by MIT, 2002).
RDBMS - favour Consistency and Availability but cannot scale horizontally
NoSQL - favour Consistency and Partitioning or Availability and Partitioning

5. Relational vs. NoSQL
Relational databases provide
- Rich query language (SQL)
- Strong consistency
- Secondary indices
NoSQL provides
- Flexibility
- “dynamic schema” allows missing or extra fields, embedded structures
- Scalability
- Adding nodes allows to scale data size
- Performance
- Adding nodes allows to scale performance
MongoDB combines both sets of advantages

6. MongoDB

7. MongoDB – a Document-Oriented DB
Open source DB on github with commercial support from MongoDB Inc (was 10gen).
• Dynamic schema (schema-less) aids agile development
• A document is an associative array (possibly more than 1-level deep), e.g. JSON Object
• Uses BSON (binary JSON format) for serialization (easy to parse)
• Binaries downloadable for Linux(es), Windows, OSX, Solaris
• Drivers available in many languages
• Provides an aggregation framework
• Is the “M” in MEAN

8. MongoDB – Performance
• Written in C++
• It achieves high performance, especially on dynamic queries
• Allows primary & secondary indices (main tuning element)
• Is horizontally scalable across many nodes
• Extensive use of memory-mapped files
i.e. read-through write-through memory caching.
• Provides high availability with Master/slave replication
• Provides Sharding (horizontal partitioning of data across nodes)
• Horizontal partitioning : complete documents (or rows) are stored on a node
• Vertical partitioning: documents (or rows) are split across nodes

9. MongoDB – Architecture

10. MongoDB – Terminology
RDBMS MongoDB
Table, View ➜ Collection
Row ➜ Document
Index ➜ Index
Join ➜ Embedded Document
Foreign Key ➜ Reference
Partition ➜ Shard

11. MongoDB – Enterprise Products
There are supported products for the enterprise
• MongoDB Enterprise: for production workloads
• MongoDB Compass: Data and Schema visualization tool
• MongoDB Connector for BI:
Allows users to visualize their MongoDB Enterprise data using existing relational
business intelligence tools such as Tableau.
Foreign data wrapper with PostgreSQL to provide a relational SQL view on
MongoDB data

12. MongoDB v3.2: new features
• The default storage engine is now WiredTiger
• Following acquisition of WiredTiger, Inc.
• Introduced as an optional engine in MongoDB 3.0
• Has more granular concurrency control and native compression (lowering storage costs,
increasing h/w utilization, throughput and providing more predictable performance)
• Replication election enhancements
• Config servers as replica sets
• readConcern, and document validations.
• OpsManager 2.0

13. MongoDB v3.2: new aggregation features
• $sample, $lookup
• $indexStats
• $filter,$slice,$arrayElemAt, $isArray, $concatArrays
• Partial Indexes
• Document Validation
• 4 bit testing operators
• 2 new Accumulators for $group
• 10 new Arithmetic expression operators

14. MongoDB components
Components
mongod - The database daemon process.
mongos - Sharding controller.
mongo - The database shell (uses interactive javascript).
Utilities
mongodump - MongoDB dump tool - for backups, snapshots, etc.
mongorestore - MongoDB restore a dump
mongoexport - Export a single collection to test (JSON, CSV)
mongoimport - Import from JSON or CSV
mongofiles - Utility for putting and getting files from MongoDB GridFS
mongostat - Show performance statistics

15. MongoDB University (http://university.mongodb.com/)

16. MongoDB drivers exist for many languages
12 official drivers, plus many community languages

17. MongoDB docs: (https://docs.mongodb.org/manual/)

18. Using MongoDB

19. MongoDB shell
> mongo
MongoDB shell version: 3.2.3
connecting to: test
> show dbs
Money_UK 0.000GB
aggregation_example 0.000GB
local 0.000GB
test 0.000GB
> use Money_UK
switched to db Money_UK
> show collections
CARD
SAVING

20. MongoDB shell
> use test
switched to db test
> db.newcoll.insert({ 'field1': 'an example' })
WriteResult({ "nInserted" : 1 })
> db.newcoll.find().count()
1
> db.newcoll.find()
{ "_id" : ObjectId("56cc7446bb127b86163cf226"), "field1" : "an example" }
> db.newcoll.findOne()
{ "_id" : ObjectId("56cc7446bb127b86163cf226"), "field1" : "an example" }
> db.newcoll.find({'_id':ObjectId("56cc7446bb127b86163cf226")})
{ "_id" : ObjectId("56cc7446bb127b86163cf226"), "field1" : "an example" }

21. MongoDB shell
> var doc2={'field1': 'example', 'field2': 'example2'}
> db.newcoll.insert(doc2)
WriteResult({ "nInserted" : 1 })
> db.newcoll.find().count()
2
> db.newcoll.find().pretty()
{ "_id" : ObjectId("56cc7446bb127b86163cf226"), "field1" : "an example"
}
{
"_id" : ObjectId("56cc7711bb127b86163cf227"),
"field1" : "example",
"field2" : "example2"
}

22. MongoDB indexing
Indexing is the single biggest tunable performance factor.
Can index on a single field, e.g. on ascending or descending values:
db.newcoll.ensureIndex( { ‘field1’: 1 } ) // or -1: descending
Or subfields:
db.newcoll.ensureIndex( { ‘arr.subfield’: 1 } )
Or multiple fields:
db.newcoll.ensureIndex( { ‘arr.subfield’: 1, ‘field2’: -1 } )

23. MongoDB indexing - 2
It is also possible to provide hints to the indexer, on uniqueness, sparseness or to
request index creation as a background task, e.g.
db.newcoll.ensureIndex( { ‘field1’: 1 }, { ‘unique’: true} )
db.newcoll.ensureIndex( { ‘field1’: 1 , ‘field2’: 1}, { ‘sparse’: true} )
db.newcoll.ensureIndex( { ‘field1’: 1 }, { ‘background’: true} )

24. MongoDB searching
We can search based on a single field
db.newcoll.find( { ‘field1’: ‘any match’ } )
Or subfields:
db.newcoll.find( { ‘arr.subfield’: ‘sub field match’ } )
Or multiple fields:
db.newcoll.find( { ‘arr.subfield’: ‘sub field match’, ‘field2’: ‘match2’ } )
Available indices will be used if possible

25. MongoDB sorting
We can sort the results in ascending or descending order
db.newcoll.find().sort( { ‘field1’: 1 } ) // or -1: descending
or
db.newcoll.find().sort( { ‘field1’: 1, ‘field2’: -1 } )
Available indices will be used if possible

26. MongoDB projections
We can reduce the number of returned fields (the projection).
This may enable data access directly from the index.
In this example we limit the returned fields to ‘field1’ (and the ‘_id’ index).
db.newcoll.find({ ‘field1’: ‘example’ }, { ‘field1’: 1} )
In this example we limit the returned fields to ‘field1’ (without the ‘_id’ index).
db.newcoll.find({ ‘field1’: ‘example’ }, { ‘_id’: 0, ‘field1’: 1} )
We can also override the default index
db.newcoll.find({ ‘field1’: ‘example’ }, { ‘_id’: ‘field1’ } )

27. MongoDB explain plan
We can request explanation of how a query will be handled (showing possible index use)
> db.newcoll.find().explain()
{
"queryPlanner" : {
"plannerVersion" : 1,
"namespace" : "test.newcoll",
"indexFilterSet" : false,
"parsedQuery" : {
"$and" : [ ]
},
"winningPlan" : {
"stage" : "COLLSCAN",
"filter" : {
"$and" : [ ]
},
"direction" : "forward"
},
"rejectedPlans" : [ ]
},
"serverInfo" : {
"host" : "MJBRIGHT7",
"port" : 27017,
"version" : "3.2.3",
"gitVersion" : "b326ba837cf6f49d65c2f85e1b70f6f31ece7937"
},
"ok" : 1
}

28. MongoDB special index types
• Geospatial Indexes (2d Sphere)
• Text Indexes
• TTL Collections (expireAfterSeconds)
• Hashed Indexes for sharding

29. MongoDB aggregation framework
Aggregation uses a pipeline of operations amongst
• $match
• $project
• $unwind
• $group
• $sort

30. MongoDB aggregation example
pipeline = [
{ "$project": { // Select fields of interest
'year': { "$dateToString": { 'format': "%Y", 'date': "$date" } },
'tags': 1, 'value': 1, }, },
{ "$match": { 'year': str(year) }},
{ "$group": {"_id": "$tags", "total": {"$sum": {"$abs": "$value"}}} },
{ "$sort": SON([("total", -1), ("_id", -1)]) }
]
cursor = db.collection.aggregate(pipeline)

31. MongoDB aggregation example
pipeline = [
{ "$project": {
'year': { "$dateToString": { 'format': "%Y", 'date': "$date" } },
'tags': 1, 'value': 1, }, },
{ "$match": { 'year': str(year) }}, // match on fields
{ "$group": {"_id": "$tags", "total": {"$sum": {"$abs": "$value"}}} },
{ "$sort": SON([("total", -1), ("_id", -1)]) }
]
cursor = db.collection.aggregate(pipeline)

32. MongoDB aggregation example
pipeline = [
{ "$project": {
'year': { "$dateToString": { 'format': "%Y", 'date': "$date" } },
'tags': 1, 'value': 1, }, },
{ "$match": { 'year': str(year) }},
{ "$group": {"_id": "$tags", "total": {"$sum": {"$abs": "$value"}}} }, // ‘reduce’
{ "$sort": SON([("total", -1), ("_id", -1)]) }
]
cursor = db.collection.aggregate(pipeline)

33. MongoDB aggregation example
pipeline = [
{ "$project": {
'year': { "$dateToString": { 'format': "%Y", 'date': "$date" } },
'tags': 1, 'value': 1, }, },
{ "$match": { 'year': str(year) }},
{ "$group": {"_id": "$tags", "total": {"$sum": {"$abs": "$value"}}} },
{ "$sort": SON([("total", -1), ("_id", -1)]) } // sort the results
]
cursor = db.collection.aggregate(pipeline)

34. PyMongo – ca y est, on arrive !
But first …

35. PyMongo – ca y est, on arrive !
… there are alternatives to the official PyMongo driver.
• Motor is a full-featured, non-blocking MongoDB driver for Python Tornado
applications.
• TxMongo is an asynchronous Twisted Python driver for MongoDB.

36. PyMongo – ca y est, on arrive !
… and other Python/MongoDB tools
ORM-like Layers
New users are recommended to begin with PyMongo.
Nevertheless other implementations are available providing higher abstraction.
Humongolus, MongoKit, Ming, MongoAlchemy, MongoEngine, Minimongo, Manga,
MotorEngine
Framework Tools
This section lists tools and adapters that have been designed to work with various Python
frameworks and libraries.
Django MongoDB Engine, Mango, Django MongoEngine, mongodb_beaker, Log4Mongo,
MongoLog, C5t, rod.recipe.mongodb, repoze-what-plugins-mongodb, Mongobox, Flask-
MongoAlchemy, Flask-MongoKit, Flask-PyMongo.

37. Questions?

38. Backup Slides